import argparse
import datetime
import json
import os
import random
import time
from pathlib import Path

import numpy as np
import ruamel.yaml as yaml
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist

import utils
from data import create_dataset, create_sampler, create_loader
from data.utils import save_result, coco_caption_eval
from models.blip import blip_decoder
from utils import cosine_lr_schedule


def train(model, data_loader, optimizer, epoch, device):
    # train
    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))
    header = 'Train Caption Epoch: [{}]'.format(epoch)
    print_freq = 50

    for i, (image, caption, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image = image.to(device)

        loss = model(image, caption)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate(model, data_loader, device, config):
    # evaluate
    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Caption generation:'
    print_freq = 10

    result = []
    for image, image_id in metric_logger.log_every(data_loader, print_freq, header):

        image = image.to(device)

        captions = model.generate(image, sample=False, num_beams=config['num_beams'], max_length=config['max_length'],
                                  min_length=config['min_length'])

        for caption, img_id in zip(captions, image_id):
            result.append({"image_id": img_id.item(), "caption": caption})

    return result


def main(args, config):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset #### 
    print("Creating captioning dataset")
    train_dataset, val_dataset, test_dataset = create_dataset('caption_coco', config)

    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        samplers = create_sampler([train_dataset, val_dataset, test_dataset], [True, False, False], num_tasks,
                                  global_rank)
    else:
        samplers = [None, None, None]

    train_loader, val_loader, test_loader = create_loader([train_dataset, val_dataset, test_dataset], samplers,
                                                          batch_size=[config['batch_size']] * 3, num_workers=[4, 4, 4],
                                                          is_trains=[True, False, False],
                                                          collate_fns=[None, None, None])

    #### Model #### 
    print("Creating model")
    model = blip_decoder(pretrained=config['pretrained'], image_size=config['image_size'], vit=config['vit'],
                         vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],
                         prompt=config['prompt'])

    model = model.to(device)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    best = 0
    best_epoch = 0

    print("Start training")
    start_time = time.time()
    for epoch in range(0, config['max_epoch']):
        if not args.evaluate:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)

            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])

            train_stats = train(model, train_loader, optimizer, epoch, device)

        val_result = evaluate(model_without_ddp, val_loader, device, config)
        val_result_file = save_result(val_result, args.result_dir, 'val_epoch%d' % epoch, remove_duplicate='image_id')

        test_result = evaluate(model_without_ddp, test_loader, device, config)
        test_result_file = save_result(test_result, args.result_dir, 'test_epoch%d' % epoch,
                                       remove_duplicate='image_id')

        if utils.is_main_process():
            coco_val = coco_caption_eval(config['coco_gt_root'], val_result_file, 'val')
            coco_test = coco_caption_eval(config['coco_gt_root'], test_result_file, 'test')

            if args.evaluate:
                log_stats = {**{f'val_{k}': v for k, v in coco_val.eval.items()},
                             **{f'test_{k}': v for k, v in coco_test.eval.items()},
                             }
                with open(os.path.join(args.output_dir, "evaluate.txt"), "a") as f:
                    f.write(json.dumps(log_stats) + "\n")
            else:
                save_obj = {
                    'model': model_without_ddp.state_dict(),
                    'optimizer': optimizer.state_dict(),
                    'config': config,
                    'epoch': epoch,
                }

                if coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4'] > best:
                    best = coco_val.eval['CIDEr'] + coco_val.eval['Bleu_4']
                    best_epoch = epoch
                    torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_best.pth'))

                log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                             **{f'val_{k}': v for k, v in coco_val.eval.items()},
                             **{f'test_{k}': v for k, v in coco_test.eval.items()},
                             'epoch': epoch,
                             'best_epoch': best_epoch,
                             }
                with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
                    f.write(json.dumps(log_stats) + "\n")

        if args.evaluate:
            break
        dist.barrier()

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/caption_coco.yaml')
    parser.add_argument('--output_dir', default='output/Caption_coco')
    parser.add_argument('--evaluate', action='store_true')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    args.result_dir = os.path.join(args.output_dir, 'result')

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    Path(args.result_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))

    main(args, config)
